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    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.10.003

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Analyse problems, not data: One world, one health

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Analyse problems, not data: One world, one health. / Diggle, Peter J.
In: Spatial Statistics, Vol. 28, 12.2018, p. 4-7.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Diggle, PJ 2018, 'Analyse problems, not data: One world, one health', Spatial Statistics, vol. 28, pp. 4-7. https://doi.org/10.1016/j.spasta.2018.10.003

APA

Diggle, P. J. (2018). Analyse problems, not data: One world, one health. Spatial Statistics, 28, 4-7. https://doi.org/10.1016/j.spasta.2018.10.003

Vancouver

Diggle PJ. Analyse problems, not data: One world, one health. Spatial Statistics. 2018 Dec;28:4-7. Epub 2018 Oct 29. doi: 10.1016/j.spasta.2018.10.003

Author

Diggle, Peter J. / Analyse problems, not data : One world, one health. In: Spatial Statistics. 2018 ; Vol. 28. pp. 4-7.

Bibtex

@article{9ff2238d12754ff9847827a0e9e706d8,
title = "Analyse problems, not data: One world, one health",
abstract = "The last fifty years or so have seen a transformational change in statistical methodology, from a discrete set of specific methods to a single, integrated paradigm. An early example is the seminal paper by Nelder and Wedderburn (1972) that introduced the unifying concept of the generalised linear model for independently replicated data. Later computational advances have stimulated a comparable unification for modelling data with various kinds of dependence, for example in time and/or in space. I argue that this transformation should encourage statistical scientists to change their focus from analysing data to solving problems. I give an example from an ongoing study of the acquisition of natural immunity to leptospirosis among slum-dwellers in northern Brazil.",
keywords = "Data model, Leptospirosis, Process model, Serial dilution assay",
author = "Diggle, {Peter J.}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.10.003",
year = "2018",
month = dec,
doi = "10.1016/j.spasta.2018.10.003",
language = "English",
volume = "28",
pages = "4--7",
journal = "Spatial Statistics",
issn = "2211-6753",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Analyse problems, not data

T2 - One world, one health

AU - Diggle, Peter J.

N1 - This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.10.003

PY - 2018/12

Y1 - 2018/12

N2 - The last fifty years or so have seen a transformational change in statistical methodology, from a discrete set of specific methods to a single, integrated paradigm. An early example is the seminal paper by Nelder and Wedderburn (1972) that introduced the unifying concept of the generalised linear model for independently replicated data. Later computational advances have stimulated a comparable unification for modelling data with various kinds of dependence, for example in time and/or in space. I argue that this transformation should encourage statistical scientists to change their focus from analysing data to solving problems. I give an example from an ongoing study of the acquisition of natural immunity to leptospirosis among slum-dwellers in northern Brazil.

AB - The last fifty years or so have seen a transformational change in statistical methodology, from a discrete set of specific methods to a single, integrated paradigm. An early example is the seminal paper by Nelder and Wedderburn (1972) that introduced the unifying concept of the generalised linear model for independently replicated data. Later computational advances have stimulated a comparable unification for modelling data with various kinds of dependence, for example in time and/or in space. I argue that this transformation should encourage statistical scientists to change their focus from analysing data to solving problems. I give an example from an ongoing study of the acquisition of natural immunity to leptospirosis among slum-dwellers in northern Brazil.

KW - Data model

KW - Leptospirosis

KW - Process model

KW - Serial dilution assay

U2 - 10.1016/j.spasta.2018.10.003

DO - 10.1016/j.spasta.2018.10.003

M3 - Journal article

VL - 28

SP - 4

EP - 7

JO - Spatial Statistics

JF - Spatial Statistics

SN - 2211-6753

ER -